Now you'll see the difference yourself. Below are two systems trying to predict rain - one using rules, one using machine learning.
ℹ️ What you'll do
Test both systems with different weather scenarios. Pay special attention to the edge cases marked with ⚠️ - these are scenarios that break simple rules.
Input Data:
Temperature
25°C
Humidity
85%
Actual Weather
🌧️ Rain
What Did You Notice?
After trying different scenarios, you probably noticed:
- • Rules are predictable - Same input always gives same output
- • Rules break on edge cases - “Cold BUT humid” confuses simple logic
- • ML adapts to patterns - It learned from data rather than explicit rules
- • ML isn't perfect either - It can make mistakes too!
Coding an Answer
- • You write explicit if/else logic
- • Every rule must be thought through
- • Easy to understand and debug
- • Breaks on unexpected input
- • Requires updates for new patterns
Learning an Answer
- • Computer finds patterns in data
- • Handles complexity automatically
- • Harder to interpret why it decided
- • Adapts to new situations
- • Quality depends on training data
⚠️ Important Insight
Neither approach is “better” - they're tools for different problems. Use rules when logic is simple and clear. Use ML when patterns are complex or hidden.
💡 When to Use Each Approach
- • Use Rules: Login validation, age verification, simple calculations
- • Use ML: Image recognition, language translation, recommendation systems
- • Use Both: Many real systems combine rules (for safety/compliance) and ML (for complex decisions)